The decoder from feature tensor to pixels, \(\mathcal {G}\) , takes as input the target representation \(T \in \mathbb {R}^{\frac{H_c}{4} \times \frac{W_c}{4} \times 2688}\) . \(T\)  is then fed into 4 independent branches, each responsible for producing one level of a 4-level laplacian pyramid parameterizing the output image. Each branch has virtually the same architecture (but separate parameters), consisting of five 3x3 convolutional layers with leaky ReLU [1]} activations (except the last layer, which is linear), and a linear residual 3x3 convolution [2]} directly from \(T\)  to the branch's output. All intermediate hidden states have 256 channels. The four branches differ only in number of output channels, having 48, 12, 3, and 3 output channels respectively. Transposed convolutions are applied to the first two branches to trade off channel depth for resolution (resulting in one \(H_c \times W_c \times 3\)  output and one \(\frac{H_c}{2} \times \frac{W_c}{2} \times 3\)  output). The third branch is not altered (resulting in a \(\frac{H_c}{4} \times \frac{W_c}{4} \times 3\)  output), and the output of the fourth branch is bilinearly downsampled by a factor of two (resulting in a \(\frac{H_c}{8} \times \frac{W_c}{8} \times 3\)  output). The final output image is synthesized by treating the output of the four branches as levels of a laplacian pyramid and combining them appropriately. In Figure REF  we give an overview of how we employ our architecture at inference.
